About This EpisodeDaniel Ziv, Global VP of AI and Analytics at Verint, reveals why experienced executives are making their worst decisions in decades—and how AI analytics is rewriting the rules of business intelligence. Learn the two critical frameworks that separate AI winners from losers, and why the biggest risk isn't picking the wrong technology—it's doing nothing at all.Guest BioDaniel Ziv leads AI and analytics product management and go-to-market strategy at Verint, where he helps global enterprises transform customer experience through data-driven decision-making. With two decades in the analytics space, Daniel has witnessed firsthand how AI is fundamentally changing what's possible in customer insights.Key Timestamps[00:00] - Why change is happening faster than ever before[03:04] - The Macro vs. Micro Analytics Framework explained[06:19] - Two flawed decision-making patterns destroying value[09:20] - Real ROI: $80M saved, $10M found in 48 hours[15:32] - Generative AI vs. Agentic AI: What's the difference?[21:03] - The hybrid cloud advantage (why on-prem isn't dead)[26:35] - Common misconceptions about Verint[28:49] - Daniel's advice for making AI decisions today[32:17] - Final thoughts: "Ride the dragon"Key TakeawaysThe Two Fatal Mistakes:Gut-based decisions without data - Your experience is becoming less reliable as change acceleratesAnalysis paralysis - Waiting weeks for insights while competitors move in hoursThe Macro-Micro Framework:Macro Analytics: Understand patterns across ALL interactions (the 30,000-foot view)Micro Analytics: Apply insights to individual interactions in real-timeCompanies that excel at both create significant competitive advantageReal Results:Large telecom: $80M saved + 11% sales increaseTypical deployment: $5-10M in insights found within 1-2 daysUK financial services: $5M additional revenue from loan process improvementsEnergy supplier: $2M saved through increased agent capacityGenerative → Agentic Evolution:Generative AI responds to prompts (you ask, it answers)Agentic AI breaks down goals and executes multi-step workflows autonomouslyExample: Genie Bot evolved from answering questions to analyzing, quantifying, and exporting results automaticallyAction Items for ListenersAudit your decision-making speed - Are you making gut calls or waiting too long for data?Identify one quick-win AI deployment - What could you turn on this week without changing infrastructure?Evaluate your analytics gaps - Do you have macro insights, micro operationalization, or both?Test before scaling - Start with 300 users, validate, then scale to 30,000Connect with Daniel - Reach out on LinkedIn to discuss your specific use caseConnect With Daniel ZivLinkedIn: https://www.linkedin.com/in/dziv1/About the Host Maribel Lopez brings decades of technology industry analysis experience, helping business leaders cut through hype to understand what actually works in AI, cloud, and digital transformation. https://www.linkedin.com/in/maribellopez/Subscribe & FollowIf you found this conversation valuable, subscribe for more deep dives with AI leaders who are actually deploying this technology and seeing real business results.Tags: #AI #Analytics #CustomerExperience #GenAI #AgenticAI #BusinessIntelligence #CXAutomation #DataDriven #DigitalTransformation #Verint

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